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Shoulder

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MR imaging for shoulder diseases: Effect of compressed sensing and deep learning reconstruction on examination time and imaging quality compared with that of parallel imaging.

Magnetic resonance imaging
PURPOSE: To compare capabilities of compressed sensing (CS) with and without deep learning reconstruction (DLR) with those of conventional parallel imaging (PI) with and without DLR for improving examination time and image quality of shoulder MRI for...

Deep learning classification of shoulder fractures on plain radiographs of the humerus, scapula and clavicle.

PloS one
In this study, we present a deep learning model for fracture classification on shoulder radiographs using a convolutional neural network (CNN). The primary aim was to evaluate the classification performance of the CNN for proximal humeral fractures (...

Deep Learning Diagnosis and Classification of Rotator Cuff Tears on Shoulder MRI.

Investigative radiology
BACKGROUND: Detection of rotator cuff tears, a common cause of shoulder disability, can be time-consuming and subject to reader variability. Deep learning (DL) has the potential to increase radiologist accuracy and consistency.

The assistance of BAZAR robot promotes improved upper limb motor coordination in workers performing an actual use-case manual material handling.

Ergonomics
This study aims at evaluating upper limb muscle coordination and activation in workers performing an actual use-case manual material handling (MMH). The study relies on the comparison of the workers' muscular activity while they perform the task, wit...

Magnetic resonance shoulder imaging using deep learning-based algorithm.

European radiology
OBJECTIVE: To investigate the feasibility of deep learning-based MRI (DL-MRI) in its application in shoulder imaging and compare its performance with conventional MR imaging (non-DL-MRI).

Evaluation of a deep learning-based reconstruction method for denoising and image enhancement of shoulder MRI in patients with shoulder pain.

European radiology
OBJECTIVES: To evaluate the diagnostic performance of an automated reconstruction algorithm combining MR imaging acquired using compressed SENSE (CS) with deep learning (DL) in order to reconstruct denoised high-quality images from undersampled MR im...

Combination Use of Compressed Sensing and Deep Learning for Shoulder Magnetic Resonance Imaging With Various Sequences.

Journal of computer assisted tomography
OBJECTIVE: For compressed sensing (CS) to become widely used in routine magnetic resonance imaging (MRI), it is essential to improve image quality. This study aimed to evaluate the usefulness of combining CS and deep learning-based reconstruction (DL...

Comparison of deep learning-based reconstruction of PROPELLER Shoulder MRI with conventional reconstruction.

Skeletal radiology
OBJECTIVE: To compare the image quality and agreement among conventional and accelerated periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI with both conventional reconstruction (CR) and deep learning-based r...

Deep learning algorithm for predicting subacromial motion trajectory: Dynamic shoulder ultrasound analysis.

Ultrasonics
Subacromial motion metrics can be extracted from dynamic shoulder ultrasonography, which is useful for identifying abnormal motion patterns in painful shoulders. However, frame-by-frame manual labeling of anatomical landmarks in ultrasound images is ...

Shoulder Range of Motion Measurement Using Inertial Measurement Unit-Validation with a Robot Arm.

Sensors (Basel, Switzerland)
The invention of inertial measurement units allowed the construction of sensors suitable for human motion tracking that are more affordable than expensive optical motion capture systems, but there are a few factors influencing their accuracy, such as...